Interpretation of water mass transformations diagnosed from data assimilation

This paper presents results from a global ocean model with 1/4° resolution and 36 vertical levels, forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and with applied altimetric sea level anomalies and temperature profile assimilation over the period 1992-96. Comparison wit...

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Bibliographic Details
Main Authors: Fox, Alan D., Haines, Keith
Format: Article in Journal/Newspaper
Language:English
Published: 2003
Subjects:
Online Access:https://pure.uhi.ac.uk/en/publications/18b8b902-9d5c-4d74-831c-4d92d0064a6f
https://doi.org/10.1175/1520-0485(2003)033<0485:IOWMTD>2.0.CO;2
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Description
Summary:This paper presents results from a global ocean model with 1/4° resolution and 36 vertical levels, forced with European Centre for Medium-Range Weather Forecasts (ECMWF) winds and with applied altimetric sea level anomalies and temperature profile assimilation over the period 1992-96. Comparison with World Ocean Circulation Experiment data indicates the important role of temperature profile assimilation in maintaining the sharp thermocline gradients. Diagnostics of Walin-type water mass transformations over the North Atlantic are shown, which are implied by the procedure of assimilation. It is seen that the altimeter assimilation contributes very little to water transformation but the temperature profile assimilation effectively prevents all drift in water volumes for potential temperatures θ 0 > 7°C. Furthermore, the temperature profile assimilation is effective at producing subtropical mode waters at a rate of 16 Sv, which the poor representation of surface fluxes in this model run is unable to do. The possibility for interpreting the assimilation transformation fluxes in terms of deficiencies in physical processes such as air-sea fluxes and internal mixing is then discussed. The paper represents a new use of data assimilation methodology in order to quantify the physical biases in the fundamental processes of surface forcing and mixing in a way that is independent of explicit model parameterizations.